Network Risk Assessment Research Articles

Risk Assessment of Distribution Networks Integrating Large-Scale Distributed Photovoltaics

Risk impacts on the operation of electric power distribution networks, when large amounts of distributed photovoltaics (PVs) are included, are gradually increasing to become a critical consideration for distribution networks' operations. In this paper, a risk assessment method is proposed which considers large-scale distributed PV feeding into distribution networks. Cluster partitioning is used to group large-scale distributed PV, to more accurately calculate the operational risk indicators for distribution networks with large-scale distributed PV compared to traditional risk assessment methods. A copula function is used to describe the correlation between the PV output and the load, in order to more accurately calculate power flow information compared to the traditional independent model of output and load. Node voltages over-limits and branch power flows outside boundaries are selected as the risk indicators, and a comprehensive risk indicator is introduced to more comprehensively and meaningfully evaluate the risk of distribution networks' operation. Also, the effects on the operational risk of the distribution network of load fluctuations, and, PV quantity, capacity, and location are analyzed and discussed. Finally, the IEEE34-bus system was simulated, and the results for different scenarios were calculated and analyzed. The simulation results demonstrate the effectiveness of the proposed method.

Assessment and Countermeasures of the Operation Risk of Distribution Network with High Penetration of Distributed PV

Due to the severe intermittence, volatility and uncertainty of the output power, the integration of large scale photovoltaics (PVs) will inevitably impact the power flow and node voltage of a distribution network, and confront the network with higher operation risk. This paper carries out the operation risk assessment of distribution networks with high penetration of PV units, and develops measures to offset the risk. A probabilistic power flow calculation based on Monte Carlo method is proposed in this paper at first, then the operation risk assessment model for a distribution network is established based on the risk assessment indexes of node voltage and line power flow limit violation. The specific implementation of the risk assessment method is presented. Based on these works, specific risk countermeasures are proposed. The validity of the risk assessment method and effectiveness of the risk countermeasures are verified by simulation results.

Seismic fragility assessment of highway bridges using D-vine copulas

Seismic vulnerability of highway bridges is crucial to the seismic risk assessment of highway transportation networks. A new framework for developing the fragility function at the system level utilizing the D-vine copula theory is proposed. In this methodology, first, the joint probabilistic seismic demand model (JPSDM), which represents the conditional joint distribution of various engineering demand parameters (EDPs) under each intensity measure (IM) level, is constructed using the D-vine approach; then, sample realizations of both the established JPSDM and capacity models of these considered components are compared to derive the system-level fragility curves. This methodology is illustrated in a typical two-span RC continuous girder-box bridge case, where pier columns, elastomeric pad bearings, the sliding bearings, and the abutments in both passive and active directions are considered as the major components. The results demonstrate that the performance of the optimal D-vine for characterizing the dependence structure among various EDPs is relatively superior in comparison with commonly-utilized multivariate standard copulas. The combination of the quantitatively-identified optimal D-vine copula and their conditional lognormal marginal distributions is adequate to construct the JPSDM of these five EDPs. Furthermore, the effects of different copula selections on the overall system vulnerability are well captured, which highlight the necessity for quantitative identification of the optimal D-vine copula for modeling the correlations among various EDPs.

A comprehensive framework for seismic risk assessment of urban water transmission networks

Earthquakes are natural disasters which human beings cannot control, causing significant damage to the economy and society as a whole. In particular, earthquakes affect not only buildings but also lifeline structures such as water distribution, electric power, transportation, and telecommunication networks. The interruption of these networks is critical because it can directly damage the facilities and, at the same time, cause long-term loss of the overall system for society. In recent years, there has been increasing interest in the uncertainties of ground motion, deterioration of pipelines, and interdependency of lifelines. Therefore, it is essential to predict the damage through possible earthquake scenarios and accounting for factors affecting lifeline structures. This study proposes a comprehensive framework to quantify the impact of earthquakes on the connectivity of urban water transmissions. The framework proposes the following steps to predict damage from earthquakes: (1) estimate the ground motion considering the spatial correlation, (2) propose a modified failure probability of buried pipelines considering deterioration, and (3) evaluate the seismic fragility curves of network components and the interdependency among water treatment plants, pumping plants, and substations. For numerical simulations, an actual water network system in South Korea was constructed using graph theory, and the magnitudes and locations of the epicenters were determined based on historical earthquake data. Finally, the reliability performance indicators (e.g., connectivity loss and serviceability ratio) were measured when earthquakes of various magnitudes occurred in the urban area. This framework will enable the prediction of damage from earthquakes and enhance decision making to minimize the extent of damage.

Integrated seismic risk and resilience assessment of roadway networks in earthquake prone areas

Intercity networks constitute a highly important civil infrastructure in developed countries, as they contribute to the prosperity and development of the connected communities. This was evident after recent strong earthquakes that caused extensive structural damage to key transportation components, such as bridges, overpasses, tunnels and geotechnical works, that in turn led to a significant additional loss associated with the prolonged traffic disruption. In cases of seismic events in developed societies with complex and coupled intercity transportation systems, the interdependency between citizens’ life and road functionality has further amplified the seismically-induced loss. Quantifying therefore, the resilience of road networks, defined as their ability to withstand, adapt to, and rapidly recover after a disruptive event, is a challenging issue of paramount importance towards holistic disaster risk mitigation and management. This study takes into account the above aspects of network resilience to earthquake loading and establishes a comprehensive, multi-criterion framework for mitigating the overall loss expected to be experienced by the community due to future earthquake events. The latter is decoupled into the direct structural damage-related loss and the indirect loss associated with the travel delays of the network users, as well as the wider socio-economic consequences in the affected area. In order to reflect the multi-dimensional nature of loss, a set of novel, time-variant indicators is herein introduced, while cumulative indicators are proposed for assessing the total loss incurred throughout the entire recovery period. This probabilistic risk management framework is implemented into a software to facilitate informed decisions of the stakeholders, both before and after a major earthquake event, thus prioritizing the pre-disruption strengthening schemes and accelerating the inspection and recovery measures, respectively.

Distribution Network Operation Risk Assessment Considering Various Influencing Factors

The frequent blackout caused by distribution network faults has sparked research on distribution system operation risks. For the continuous increase of operation risk influencing factors in the process of distribution network development, a risk assessment model considering various influencing factors of distribution network is proposed. In the model, a new method is adopted to calculate outage cost of distribution network, which reflects the comprehensive effect of interruption frequency, interruption duration and quantity of outage on the fault cost. The method can calculate outage cost better, and the distribution network risk assessment result is closer to actual situation. Finally, the Monte Carlo method is adopted to conduct the risk assessment of RBTS Bus - 3, and the evaluation results are compared with those of analytical methods to verify the effectiveness of the method.

The method of Network Risk Assessment based on the Extension Engineering

With the fast paced development of the network planning techniques, its application in the project management area is gaining momentum. The practice of network planning in real life projects is affected by a variety of risk factors. In order to better estimate project status with network planning techniques, the article proposes a methodology to quantify the degrees of risks and to take precautions during project execution to minimize the risk related costs and to achieve the project objectives. This methodology performs an overall evaluation of the risk factors through the extension engineering method, considering the unpredictability nature of the continuing effect of the risk factors on Program Evaluation and Review Technique (PERT)

A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment

The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

Cyclic subway networks are less risky in metropolises

Subways are crucial in modern transportation systems of metropolises. To quantitatively evaluate the potential risks of subway networks suffered from natural disasters or deliberate attacks, real data from seven Chinese subway systems are collected and their population distributions and anti-risk capabilities are analyzed. Counterintuitively, it is found that transfer stations with large numbers of connections are not the most crucial, but the stations and lines with large betweenness centrality are essential, if subway networks are being attacked. It is also found that cycles reduce such correlations due to the existence of alternative paths. To simulate the data-based observations, a network model is proposed to characterize the dynamics of subway systems under various intensities of attacks on stations and lines. This study sheds some light onto risk assessment of subway networks in metropolitan cities.

Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method

China’s urban rail transit (URT) construction is coming into the stage of rapid development under the guidance of national policies. However, the URT construction projects belong to high-risk projects and construction safety accidents occur frequently. Presently, safety risk management is in continuous development. Unfortunately, due to risk data deficiencies and lack of relationship between participants and safety risk factors, most of the research results cannot be well applied to URT projects. To overcome the limits, this paper has applied the text mining method into safety risk analysis. Through word frequency analysis and cluster analysis, 15 safety risk factors and 3 participants are identified from 156 accident reports. In addition, the accident descriptive model has been established, which is composed of indirect safety risk factors (management defects), direct safety risk factors and participants. In this model, each accident is the standardized description of the corresponding accident information. This is useful for risk data accumulation and analysis. Then the network structure analysis and risk assessment methods are utilized to make clear 63 relationships among participants, management defects and direct safety risk factors. Subsequently, the risk value of each relationship is evaluated. These safety risk information is integrated into the accident descriptive model by using accident points. Finally, ABC analysis which is a popular and effective method used to classify items into specific categories that can be managed and controlled separately is used to analyze the safety risk management’s core process(A), important process(B) and general process(C) in the accident descriptive model. The research results show that the constructor should pay attention to construction coordination, safety specifications, safety measures and personnel education, the supervisor should attach importance to timely communication, the monitoring unit should pay attention to advanced forecast and dynamic control. The main research contributions are as follows: (1) A method of obtaining risk data from unstructured content has been provided; (2) The accident descriptive model could be utilized for risk data continuous accumulation; (3) The emphases of URT construction safety risk management are made clear.

Multivariate Probabilistic Seismic Demand Model for the Bridge Multidimensional Fragility Analysis

Seismic fragility analysis for bridges is an essential issue for risk assessment of transportation networks exposed to seismic hazards. Considering multiple Performance Limit States (PLSs) and seismic demand parameters, the study proposes a multidimensional fragility evaluation methodology for engineering structures, and the objective of the paper is to show that the uncertainty and dependence between seismic demand parameters should be considered for fragility analysis. Thus, a new Probabilistic Seismic Demand Model (PSDM) following multivariate logarithmic normal distribution is addressed. Taking PLS correlation into consideration, multidimensional PLS formula is constructed to identify the structural failure domain. A RC bridge is studied to show the proposed theory. To consider bridge column plastic deformation and bearing nonlinear characteristic, nonlinear dynamic analyses are carried out. The bridge multidimensional fragility curves are derived and compared with fragility curves for an individual component. Results indicate that uncertainty and dependence of demand parameters can be properly dealt with by the multivariate PSDM. The multidimensional fragility is higher than fragility of any individual component, and the bridge as a system is more fragile. The ignorance of multiple components contribution to the system will generate an overestimation for the whole structural performance, which is adverse to engineering structural safety.

Analysis of information-telecommunication network risk based on cognitive maps and cause-effect diagram

The subject matter of the article is the processes of analysis and risk assessment of information and telecommunications networks. The aim is to reduce the potential losses caused by the risks of information and telecommunications network (ITN) functioning by taking timely risk management measures. The objectives are: classification of ITN risks, highlighting the main factors and causes of their occurrence; formation of a systematic presentation of risks to identify their manifestation and consequences; development of the method for assessing the influence of the risk and private risk on probable consequences; obtaining a quantitative risk assessment of ITN. The methods used are: system analysis of risks, method of cognitive maps, cause-and-effect analysis. The following results are obtained: classification of private risks of ITN according to the reasons and the factors of their occurrence is made; the negative consequences affecting the basic characteristics of the operation of ITN are defined; as a result, the structural system model of ITN risks is formed, in which the relationships between the elements of the main aspects of risk are shown; the method based on the theory of causal analysis is suggested in order to quantify the risk impact on ITN functioning. The risk model is based on the construction and analysis of probabilistic or fuzzy cognitive maps. Experts estimate the level of influence of private risks on the characteristics of the network in order to make decisions on risk management. The generalized structure of the cause-effect diagram of the risk factors, manifestation and consequences is developed; on ITN basis the method for quantifying the probability of risk consequences is suggested. The quantitative assessment of probable malfunctioning of the network that is determined by a specific effect (taking into account ITN probability), which is caused by private risks is also made. Conclusion. The suggested approach for quantitative assessment of ITN risk is based on the method of cause-and-effect analysis and enables taking into account both the factors causing it and probable consequences. The obtained results can be used to determine probable failures and losses in ITN functioning on the basis of the information about the degree of risk factors effects, risk events and consequences, and the cause-effect relationships between them. Thus, potential losses can be identified; measures to manage the risks of ITN functioning can be taken.

The Application of W. Kent Muhlbauer’s Model For The Risk Assessment of District Heating Networks

The Application of W. Kent Muhlbauer’s Model For The Risk Assessment of District Heating Networks

Systemic Risk Assessment in Complex Supply Networks

The growth in size and complexity of supply chains has led to compounded risk exposure, which is hard to measure with existing risk management approaches. In this study, we apply the concept of systemic risk to show that centrality metrics can be used for complex supply network risk assessment. We review and select metrics, and set up an exemplary case applied to the material flow and contractual networks of Honda Acura. In the exemplary case study, geographical risk information is incorporated to selected systemic risk assessment metrics and results are compared to assessment without risk indicators in order to draw conclusions on how additional information can enhance systemic risk assessment in supply networks. Katz centrality is used to measure the node's risk spread using the World Risk Index. Authority and hub centralities are applied to measure the link risk spread using distances between geographical locations. Closeness is used to measure speed of disruption spread. Betweenness centrality is used to identify high-risk middlemen. Our results indicate that these metrics are successful in identifying vulnerabilities in network structure even in simplified cases, which risk practitioners can use to extend with historical data to gain more accurate insights into systemic risk exposure.

Data-driven analytics for cyber-threat intelligence and information sharing

Efficient analysis of shared Cyber Threat Intelligence (CTI) information is crucial for network risk assessment and security hardening. There is a growing interest in implementing a proactive line of defense through threat profiling. However, determining the resiliency of a particular network with respect to relevant threats reported in CTI shared data remains a challenge, largely due to the lack of semantics and contextual information present in textual representations of the threat knowledge. To overcome the limitations of existing CTI frameworks, we devise a threat analytics framework based on Web Ontology Language (OWL) for formal specification, semantic reasoning, and contextual analysis, allowing the derivation of network associated threats from large volumes of shared threat feeds. Our ontology represents constructs of Structured Threat Information eXpression (STIX) with the additional concepts of Cyber Observable eXpression (CybOX), network configurations, and Common Vulnerabilities and Exposure (CVE) for risk analysis and threat actor profiling. The framework provides an automated mechanism to investigate cyber threats targeting the network under question by classifying the threat relevance, determining threat likelihood, identifying the affected and exposed assets through formulated rules and inferences. We perform a comprehensive structural and conceptual evaluation of critical advanced persistent threats (APTs) collected from credible sources and determine their relevance and risk posed to realistic network case studies. Finally we show that the proposed framework is novel in the type of analytics it provides and outperforms other competing approaches in terms of efficiency and effectiveness.

Probabilistic seismic risk forecasting of aging bridge networks

Bridges are the most vulnerable elements in transport systems, and they may undergo structural problems due to environmental conditions and natural disasters. Governmental agencies and owning companies must therefore plan maintenance and retrofit interventions rationally, to avoid potential severe network disruptions. With reference to seismic risk, several studies on the risk assessment of bridge networks, and on aging as one of the main factors affecting the seismic vulnerability of existing bridges, have recently also been reported. In these contributions, the seismic fragility of bridges is considered as a time-dependent parameter, whereas seismic hazard and financial exposure are described according to classic stationary assumptions. The present study proposes an innovative, comprehensive and fully time-dependent probabilistic seismic risk framework, to evaluate the expected average annual loss for stocks of deteriorating bridge structures. This framework is illustrated in a case study of 500 bridges and the results are critically discussed.

In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian

Abstract The traffic conflict technique (TCT) was developed as “surrogate measure of road safety” to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applications of TCT focus on a specific site by the way of manually or automated supervision. Nowadays the development of in-vehicle (IV) technologies provides new opportunities for monitoring driver behavior and interaction with other road users directly into the traffic stream. In the paper a stereo vision and GPS system for traffic conflict investigation is presented for detecting conflicts between vehicle and pedestrian. The system is able to acquire geo-referenced sequences of stereo frames that are used to provide real time information related to conflict occurrence and severity. As case study, an urban bus was equipped with a prototype of the system and a trial in the city of Catania (Italy) was carried out analyzing conflicts with pedestrian crossing in front of the bus. Experimental results pointed out the potentialities of the system for collection of data that can be used to get suitable traffic conflict measures. Specifically, a risk index of the conflict between pedestrians and vehicles is proposed to classify collision probability and severity using data collected by the system. This information may be used to develop in-vehicle warning systems and urban network risk assessment.

Cross-entropy-based Adaptive Importance Sampling for Probabilistic Seismic Risk Assessment of Lifeline Networks Considering Spatial Correlation

There exist pressing research needs for developing sophisticated methodologies for probabilistic seismic risk assessment (PSRA) of complex lifeline networks, which are critical backbones of today's urban communities. In particular, ground-motion intensities at different locations of lifeline networks show significant variability and spatial-correlation, which should be properly considered in evaluating structural damage of lifeline networks and their corresponding network-level performance. In addition, the probabilities of the post-disaster states of the network often shows large variability, so simulation-based approaches may require a huge number of computational simulations if the set of the possible network states includes rare events. To improve the efficiency of simulation-based approach while maintaining its broad applicability, adaptive importance sampling methods have been recently introduced. In this study, a new PSRA approach is developed for lifeline networks by extending a cross-entropy-based adaptive importance sampling (CE-AIS) to estimate the probabilities of multiple possible states of the network utility concurrently. To test and demonstrate the proposed approach, a hypothetical traffic network under seismic hazard from surrounding active faults is investigated. The results of the numerical example show that the proposed approach, termed as CE-based concurrent AIS (CE-CAIS), identifies a near-optimal sampling density through only a few rounds of pre-sampling and drastically improves efficiency of simulation-based PSRA. It is also noted that CE-CAIS does not rely on a subjective intuition to select importance sampling density or require additional reliability analysis to identify important regions. The proposed method is expected to improve PSRA of lifeline networks in terms of efficiency and applicability, and provide new insights into the risk assessment and management of such urban infrastructures.

Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles

Electric vehicles (EVs) have received wide attention due to their higher energy efficiency and lower emissions. However, the random charging and discharging behaviors of substantial numbers of EVs may lead to safety risk problems in a distribution network. Reasonable price incentives can guide EVs through orderly charging and discharging, and further provide a feasible solution to reduce the operational risk of the distribution network. Considering three typical electricity prices, EV charging/discharging load models are built. Then, a Probabilistic Load Flow (PLF) method using cumulants and Gram-Charlier series is proposed to obtain the power flow of the distribution network including massive numbers of EVs. In terms of the risk indexes of node voltage and line flow, the operational risk of the distribution network can be estimated in detail. From the simulations of an IEEE-33 bus system and an IEEE 69-bus system, the demonstrated results show that reasonable charging and discharging prices are conducive to reducing the peak-valley difference, and consequently the risks of the distribution network can be decreased to a certain extent.

Exposing the role of exposure: Public transport network risk analysis

Network risk assessment takes into consideration the probability that adverse events occur and the impacts of such disruptions on network functionality. In the context of transport networks, most studies have focused on vulnerability, the reduction in performance indicators given that a disruption occurs. This study presents and applies a method to explicitly account for exposure in identifying and evaluating link criticality in public transport networks. The proposed method is compared with conventional measures that lack exposure information. A criticality assessment is performed by accounting for the probability of a certain event occurring and the corresponding welfare loss. The methodology was applied for a multi-modal public transport network in the Netherlands where data concerning disruptions was available. The results expose the role of exposure in determining link criticality and overall network vulnerability. The findings demonstrate that disregarding exposure risks prioritizing links with high passenger volumes over links with a higher failure probability that are significantly more critical to network performance. The inclusion of exposure allows performing a risk analysis and has consequences on assessing mitigation measures and investment priorities.